Which of the following can be used to control for the effect of confounding?
A confounding variable is an “extra” variable that you didn’t account for. They can ruin an experiment and give you useless results. They can suggest there is correlation when in fact there isn’t. They can even introduce bias. That’s why it’s important to know what one is, and how to avoid getting them into your experiment in the first place. Show
In an experiment, the independent variable typically has an effect on your dependent variable. For example, if you are researching whether lack of exercise leads to weight gain, then lack of exercise is your independent variable and weight gain is your dependent variable. Confounding variables are any other variable that also has an effect on your dependent variable. They are like extra independent variables that are having a hidden effect on your dependent variables. Confounding variables can cause two major problems:
Let’s say you test 200 volunteers (100 men and 100 women). You find that lack of exercise leads to weight gain. One problem with your experiment is that is lacks any control variables. For example, the use of placebos, or random assignment to groups. So you really can’t say for sure whether lack of exercise leads to weight gain. One confounding variable is how much people eat. It’s also possible that men eat more than women; this could also make sex a confounding variable. Nothing was mentioned about starting weight, occupation or age either. A poor study design like this could lead to bias. For example, if all of the women in the study were middle-aged, and all of the men were aged 16, age would have a direct effect on weight gain. That makes age a confounding variable. Confounding BiasTechnically, confounding isn’t a true bias, because bias is usually a result of errors in data collection or measurement. However, one definition of bias is “…the tendency of a statistic to overestimate or underestimate a parameter”, so in this sense, confounding is a type of bias. Confounding bias is the result of having confounding variables in your model. It has a direction, depending on if it over- or underestimates the effects of your model:
How to Reduce Confounding VariablesMake sure you identify all of the possible confounding variables in your study. Make a list of everything you can think of and one by one, consider whether those listed items might influence the outcome of your study. Usually, someone has done a similar study before you. So check the academic databases for ideas about what to include on your list. Once you have figured out the variables, use one of the following techniques to reduce the effect of those confounding variables:
Related Articles:Age Graded Influences ReferencesKotz, S.; et al., eds. (2006), Encyclopedia of Statistical Sciences, Wiley. CITE THIS AS: Need help with a homework or test question? With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. Your first 30 minutes with a Chegg tutor is free! Which of the following can be used for controlling confounding variables?randomization. Randomization is a scientific technique by which the effect of confounding can be reduced since confounding cannot be assumed as a constant.
What is the best way to control confounding variables?The ideal way to minimize the effects of confounding is to conduct a large randomized clinical trial so that each subject has an equal chance of being assigned to any of the treatment options.
Which of the following methods can be used to control for confounding in observational studies?Answer and Explanation: The correct statement is (d) adjustment. Confounding variables are controlled in design using the following methods, namely randomization, restriction, and matching.
Which of the following is not a method to control for the effects of confounding?The correct statement is (d) blinding. Confounding is a type of systematic error that occurs in epidemiology.
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